cs.AI updates on arXiv.org 07月21日 12:06
H-NeiFi: Non-Invasive and Consensus-Efficient Multi-Agent Opinion Guidance
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针对社交媒体舆论引导挑战,提出H-NeiFi框架,通过分层非侵入式方法,结合角色行为模型与自适应邻居过滤,提高共识速度,避免直接干预用户,实现高效舆论引导。

arXiv:2507.13370v1 Announce Type: cross Abstract: The openness of social media enables the free exchange of opinions, but it also presents challenges in guiding opinion evolution towards global consensus. Existing methods often directly modify user views or enforce cross-group connections. These intrusive interventions undermine user autonomy, provoke psychological resistance, and reduce the efficiency of global consensus. Additionally, due to the lack of a long-term perspective, promoting local consensus often exacerbates divisions at the macro level. To address these issues, we propose the hierarchical, non-intrusive opinion guidance framework, H-NeiFi. It first establishes a two-layer dynamic model based on social roles, considering the behavioral characteristics of both experts and non-experts. Additionally, we introduce a non-intrusive neighbor filtering method that adaptively controls user communication channels. Using multi-agent reinforcement learning (MARL), we optimize information propagation paths through a long-term reward function, avoiding direct interference with user interactions. Experiments show that H-NeiFi increases consensus speed by 22.0% to 30.7% and maintains global convergence even in the absence of experts. This approach enables natural and efficient consensus guidance by protecting user interaction autonomy, offering a new paradigm for social network governance.

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舆论引导 社交媒体 共识速度 非侵入式方法 多代理强化学习
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